Public Equity Finance and Firm Growth: A Unique Test Using Data from U.S. Commercial Banks

Robert DeYoung

University of Kansas, Lawrence, KS 66045 USA rdeyoung@ku.edu

Lei Li

University of Kansas, Lawrence, KS 66045 USA lei.li@ku.edu

This draft: September 16, 2014

Comments are welcome

Abstract

Publicly traded firms have access to larger, less expensive and more liquid equity capital markets.

Lower capital costs should allow public firms to grow faster than privately held firms, but this advantage may be reduced or even offset by the agency costs associated with the separation of ownership and control. The net result of this tradeoff is difficult to test empirically, however, due to the potential endogeneity of both firm status (publicly traded versus privately held) and firm growth opportunities.

We devise a pair of simple solutions to neutralize potential endogeneity, and we estimate the resulting model for a matched set of publicly traded and privately held U.S. commercial banking companies observed quarterly from 1984 through 2012. The banking industry provides an especially good laboratory for our tests given that, among other advantages, federal regulators collect voluminous financial data for both listed and unlisted banks.

We find that publicly traded banking companies are able to respond more fully to growth opportunities than their privately held peers, but only during periods of slow macroeconomic growth when internal capital is scarce. While loan growth at public banks appears to suffer from short-termism—we find evidence that strong growth opportunities are followed by periods of reduced interest income and increased loan losses at these banks—associated increases in bank earnings more than offset the asset write-downs. Consistent with having ready currency for acquisitions, public banks tend to exploit growth opportunities via external (M&A) growth channels, while private banks tend to exploit growth opportunities via organic growth channels. We also find evidence suggesting that public banks grow their loan portfolios more slowly than private banks during economic downturns.

This study provides empirical support for the seldom-tested conventional finance wisdom that access to public capital markets allows firms to grow faster. Our findings also help explain the surge in bank IPO activity during the 1980s, have implications for counter-cyclical bank capital regulation (Basel III), and contribute to the debate about credit supply shocks during recessions. * We thank seminar participants at DePaul University, the International Finance and Banking Society, the University of Kansas, and Louisiana State University for their comments and suggestions.

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1. Introduction

The limited liability corporation is one of the oldest and perhaps the most important of all

financial innovations. The notion that a firm’s growth capacity is enhanced by having access to public

funding markets—in which investors’ personal wealth is legally separable from their public

investments—is nearly axiomatic among finance scholars.1 Publicly traded firms tend to have less

concentrated ownership, exhibit less information asymmetry and (by definition) have greater access to

liquid capital markets than do privately held firms. These factors predict that publicly traded firms should

have a lower cost of external capital, a prediction that is confirmed by many empirical studies (e.g.,

Pagano, Panetta, and Zingales 1998; Brav 2009; Saunders and Steffen 2011). In the absence of

substantial distortions related to the separation of ownership and control (Berle and Means 1932, Jensen

and Meckling 1976, Jensen 1986, Stein 1989, Cronqvist and Nilsson 2003), lower capital costs should

enable public firms to respond more fully to growth opportunities when they occur.

A large body of work, typically referred to as the IPO (initial public offering) literature, examines

private firms’ decisions to go public and the inefficiencies associated with the pricing of IPO shares.2 A

far larger body of work, typically referred to as the corporate governance literature, examines the

principal-agent inefficiencies associated with the separation of ownership from control in public firms.3

But only a few studies have tested the notion that access to public capital markets makes publicly traded

firms better able than privately held firms to exploit growth opportunities. The primary reason for this

paucity of studies, of course, is that researchers cannot access financial data or accounting data for

privately held firms. To the best of our knowledge, only three relatively recent studies compare the

investment behavior at privately held versus publicly traded firms, with mixed results. Sheen (2009)

focuses on firms in the chemical industry, and finds that private firms are better able to predict future

demand shocks than publicly traded firms. Gilje and Taillard (2013) focus on firms in the natural gas

1 While large, privately held firms do exist in market economies—for example, Cargill, Koch Industries, Hallmark, Mars, Pilot Flying J, US Foods, Cox Enterprises, Toys ‘R’ Us, Aramark (Forbes 2012)—these are exceptions that seemingly prove the rule. 2 See Ritter (2011) for a recent review of the IPO literature. 3 See Yermack (2010) and Frydman and Jenter (2010) for recent reviews of the corporate governance literature.

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industry, and find that publicly traded firms are more responsive to investment opportunities than their

privately held peers. Asker, Farre-Mensa, and Ljungqvist (2013) examine a matched sample of private

and public firms across multiple industries, and find that private firms are more responsive than public

firms to changes in investment opportunities.

Each of these studies faces two non-trivial econometric challenges. First, firms in any industry

are not randomly assigned to private and public status; those that have chosen to go public likely did so

because they faced relatively large investment opportunities that could not be fully funded by private

investors. Hence, the public versus private ownership status of the firms being studied is almost surely

endogenous to their growth and investment decisions observed in the data. None of the above studies

address this source of potential endogeneity. Second, the investment opportunities faced by the firms

being studied are likely to have a large idiosyncratic component; as a result, the growth opportunities

observed in the data are unlikely to be exogenous to the firms’ growth and investment decisions. Of the

studies cited above, only Gilje and Taillard (2013) is able to convincingly address this source of potential

endogeneity.4 In this study, we employ an empirical strategy that more fully addresses both of these

econometric challenges than in past studies. In doing so, our investigation results in a richer description

of how public ownership influences the ability of firms to exploit growth opportunities, the channels

through which firms pursue growth, the profitability of the resulting growth, and the potential spillover

effects of firm growth patterns on firms in other industries.

We deploy our approach for a large matched sample of publicly traded and privately held U.S.

commercial banking companies between 1984 and 2012. The dependent variable in our tests is the

annualized quarterly growth rate in bank assets (or bank loans), which we disaggregate into organic

(internal) growth and external (M&A) growth components in separate tests. There are two key test

variables, which we specify interactively in our regressions: a dummy variable that indicates a publicly

traded bank and a continuous variable that captures bank-specific growth opportunities. We take ex ante

4 The authors use changes in natural gas prices and county-specific shale gas discoveries as instruments for exogenous identification of firms’ growth opportunities.

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precautions to ensure that neither of these two right-hand side test variables is endogenous to bank growth

rates. First, we draw a strong distinction between a bank ‘going public’ (a state of nature that is prone to

endogeneity bias) and a bank ‘being public’ (the state of nature that we wish to observe in our tests). We

impose this distinction by including in our data only those publicly traded banks with IPOs that occurred

more than ten years (40 quarters) in the past. Second, we measure bank-specific growth opportunities as

the quarterly change in local macroeconomic conditions within each bank’s unique geographic footprint.

This broad measure of lending conditions is clearly exogenous to the activities of individual banks, and it

is an appropriate measure of growth opportunities because the demand for banks’ main investment (loans)

is driven by the economic conditions faced by banks’ business and household customers.

We find a number of interesting and robust patterns in the data. Publicly traded banks are better

than privately held banks at exploiting changes in local growth opportunities under certain broad and

theoretically plausible conditions. When local macroeconomic growth is slow, public banks are better

able than private banks to embrace growth opportunities; we trace this advantage to a reductions in banks’

internal cash flows during these periods, which public banks can circumvent (but private banks cannot)

via their access to public funding markets. This relative growth advantage disappears under strong local

macroeconomic conditions when both public and private banks can finance growth less expensively using

retained earnings. We also find a distinct public-private growth channel dichotomy. Public banks tend to

use the external growth channel (M&As) to grow their assets and loans, while private banks tend to use

organic growth channels. This is a reasonable result given that bank acquisitions are typically transacted

by issuing new equity shares (as opposed to using cash) and may help explain the surge of commercial

bank IPOs in the U.S. during the late-1980s and throughout the 1990s: banks wishing to take advantage

of the M&A growth opportunities being made possible by deregulation gained access to the currency

(cheaper equity capital) necessary to make those transactions. This is not to say that public banks eschew

the organic growth channel—indeed, when access to public capital markets is most valuable (i.e., when

the economy is growing slowly and bank earnings are low), public banks respond to new growth

opportunities through both external and organic channels.

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Are these observed growth differentials at public banks a symptom of underlying principal-

agency problems? The data suggest otherwise. While loan growth at public banks appears to suffer from

short-termism—that is, episodes of strong exogenous growth opportunities are followed by periods of

reduced interest income and increased loan losses at these banks—the incremental bank earnings that

accrue to public banks during these periods more than offset the asset write-downs. While these findings

are based on circumstantial accounting evidence, and not on direct tests of the bank growth-bank value

relationship, they are strongly suggestive.

Finally, we show that the well-known pro-cyclicality of bank loan growth is asymmetric for

public and private banks. Public banks grow their loans substantially slower than do private banks during

local economic recessions (i.e., at and below the 10th percentile of local macroeconomic growth). Hence,

public bank loan supply appears to be more pro-cyclical than private bank loan supply—a potentially

important finding as bank regulators (i.e., Basel III) seek to impose counter-cyclicality on bank equity

capital requirements—which infers that privately held banks may be disproportionately important for

maintaining credit supply during economic downturns.

The remainder of the paper is organized as follows. In section 2 we present five reasons why the

banking industry is a natural laboratory for examining these questions. In section 3 we present our

empirical research methodology and main testable hypotheses. In section 4 we describe the data and the

variables used in our tests. Our main results are presented in section 5, followed by the presentation of

robustness results in section 6. Section 7 concludes.

2. Banking industry as a laboratory

Limiting the scope of an empirical study to firms in a single industry (banking or otherwise)

reduces the amount of unexplained heterogeneity in the data. This being said, most empirical finance

studies avoid the banking industry over concerns that, as a highly regulated industry, findings from the

banking industry may not generalize to firms in other industries. On the contrary, there is a long list of

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reasons that make the banking industry an outstanding laboratory for investigating the questions at hand

in this study.

First, the size distributions of publicly traded and privately held U.S. commercial banks overlap

to a degree not found in other industries (see Figure 1). As well, public and private banks overlap

substantially in terms of business mix, geographic location, and other competitive characteristics.

Regulation strictly limits the lines of business that banks (public or private) can enter into; deposit

insurance permits privately held banks to issue substantial amounts of debt (deposits) at the exact same

prices as publicly traded banks; and the use of private equity or venture capital finance has generally been

prohibited. Hence, differences in public and private banks’ growth and investment decisions should be

more directly attributable to differences in capital market access than in other industries.

Second, the growth opportunities facing individual banks can be more accurately measured than

in most other industries. Banks’ main investments, loans, are usually made to firms and households

within well-defined geographic footprints. The demand for these loans is driven by local economic

conditions, which are easily observable, exhibit both cross-section and time-series variation, and are

clearly exogenous to the banks. In our tests, we use this exogenous variation as a very clean proxy for

bank-specific investment opportunities.

Third, the U.S. banking industry experienced a substantial consolidation during the past three

decades which reduced the population of commercial banks by about one half. This consolidation was

accomplished largely via merger and acquisition. This provides us with two separate channels of bank

investment—organic growth achieved via existing or newly built branch and internet networks, and

external growth via acquisition of other banking companies—and allows us to test whether and how

access to public capital markets is differentially expressed though these two channels.

Fourth, bank investment activity (chiefly, making loans) can respond quickly to changes in

growth opportunities and we can easily and accurately observe this investment activity in the data. Banks

file highly detailed quarterly financial reports with federal regulators, and the contents of these reports are

uniform across publicly traded and privately held banking companies. These detailed data extend back to

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the mid-1980s, providing us with a long time series of data over multiple business cycles with rich

variation growth opportunities.

Fifth, non-banking firms require bank loans—along with other inputs—to exploit their own

investment and growth opportunities. By studying whether and how bank access to public capital

markets influences bank lending behaviors, we will also be generating evidence regarding whether and

how bank access to public capital markets dampens or amplifies the business cycle.

3. Test methodology

We employ a straightforward empirical model that neutralizes the endogeneity discussed above

and cleanly identifies the hypothesized relationships among capital market access, ex ante growth

opportunities, and ex post actual growth:

%GROWTHi,t+1 = a + b*PUBLICi + c*OPPORTUNITIESi,t +

d1*PUBLICi*OPPORTUNITIESi,t + Controlsi,t + ui*vt + ei,t+s (1)

where i indexes banks and t indexes time in quarters. The dependent variable %GROWTHi,t+1 measures

the percentage growth in bank i’s assets (or loans) from quarter t through quarter t+1. PUBLICi is a

dummy variable that equals 1 for publicly traded banks and 0 for privately held banks, and is the

treatment variable in our tests. OPPORTUNITIESi,t measures the temporary or intermittent growth

opportunities facing bank i at time t, and is the main control variable in our tests. Controlsi,t is a vector of

additional bank- and time-varying exogenous variables, ui are geographic (home state) fixed effects, vt are

time fixed effects, and ei,t+1 is a symmetric error term with zero mean.

Including state-time fixed effects is especially important to control for the impact of state and

federal deregulation on bank growth rates. Between the mid-1970s and the mid-1990s, most of the fifty

states relaxed or eliminated regulations that had previously restricted their banks from operating freely

within or across state borders. This deregulatory movement culminated with the passage of the Reagle-

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Neal Act of 1994 which, within a few years of its passage, eliminated the major remaining barriers to

geographic expansion for banks in all states. These deregulatory moments provided temporary growth

opportunities for banks—in a way, providing a release for banks’ pent-up demand for expansion—that are

separate and distinct from the growth opportunities provided by strong local economic conditions.5

Equation (1) is rife with potential for endogeneity bias. If not carefully specified, the variable

PUBLIC is likely to be correlated with the residual term e because banks more able to grow (banks with

strong management, banks located in states with growth-friendly regulations, etc.) are more likely to have

‘gone public’ than other banks. Similarly, if not carefully specified, the variable OPPORTUNITIES is

likely to be correlated with e because a bank that is experiencing growth is likely to uncover more

potential new investment opportunities (by virtue of interacting with a greater number of clients) than

banks that are not growing. We take care to specify both of these variables in a fashion that mitigates the

potential for endogeneity in our model.

First, we differentiate between ‘going public’ and ‘being public.’ Almost by definition, a firm

‘goes public’ in response to substantial current growth opportunities for which it lacks the necessary

equity capital to exploit. Theoretical work by Clementi (2002) predicts that firms go public in response to

permanent positive productivity shocks that result in a higher minimum efficient scale of operation. This

suggests that firms will experience temporary increases in growth (until they achieve their new larger

efficient size), after which growth will return to pre-shock levels. Evidence uncovered by two recent

empirical studies is consistent with this theory. Using data on over 2,500 U.S. firms that went public

between 1972 and 2000, Chemmanur, He and Nandy (2009) find that total factor productivity increased

on average during the five years prior to their IPOs, then declined during the five years following their

IPOs. Using data on all newly public firms in the U.S. between 1996 and 2010, Kenney, Patton and

Ritter (2012) report that real revenues at these firms grew at a 10.8% compounded annual rate during the

first three post-IPO years, 6.7% during the first five post-IPO years, and 4.8% during the first ten post-

5 We also estimated separately specified versions of our model to test whether publicly traded banks were better able than privately held banks to exploit the temporary growth opportunities embedded in state and/or federal deregulatory events. These tests are described in the Robustness Tests section below.

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IPO years. Based on these figures, it is easy to show that firm revenues were growing by at most a 2.96%

real rate six-to-ten years after going public.6 Hence, we set PUBLIC = 1 for banks that have been

publicly traded for more than ten years; set PUBLIC = 0 for privately held banks; and drop from our

sample banks that have been publicly traded for ten years or less. We maintain that this ten-year

restriction pushes the going public decision far enough back into the past so that it is no longer related to

current growth opportunities.

Second, we differentiate conceptually between (a) growth opportunities that are idiosyncratic to a

single bank and (b) those that are available to any bank with the investment capital necessary to exploit

them. Regarding the former, there are only two ways that an idiosyncratic project opportunity can arise:

either by random luck or (more likely) due to actions taken by the bank in question. Thus, idiosyncratic

investment opportunities will tend to be endogenous in our model. Regarding the latter, there are only

two ways that a common-access project can arise: either via cooperative actions taken by all banks or

(much more likely) due to an event or process that occurs outside the control of banks. We define growth

opportunities as the macroeconomic environment that each bank faces in the geographic markets in which

it operates. Defined thusly, the variable OPPORTUNITIES is clearly exogenous to banks’ investment

decisions and is clearly not idiosyncratic to individual banks in any given geographic area.

3.1. Growth hypotheses. Finance theory suggests two important differences between publicly

traded and privately held firms. On-the-one-hand, access to less expensive sources of capital makes it

possible for the firm to grow faster, by increasing the value of marginal investment projects. On-the-

other-hand, control issues associated with the fragmentation of ownership at public firms can also

influence firm growth. Reduced shareholder monitoring of management could lead to higher-than-

optimal growth (e.g., empire building management accepts negative net present value projects) or lower-

than-optimal growth (e.g., risk averse management rejects risky but positive net present value projects).

Higher-than-optimal growth could also stem from short-term management incentives to send positive

6 Solving the following equality for X yields the 2.96% average real compounded annual revenue growth for post-IPO years six through ten: (1+0.067)5(1+X)5 = (1+0.048)10.

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earnings signals to capital markets. While the net impact of these effects on firm growth may be either

positive or negative, as a general matter let us characterize the public firm growth advantage hypothesis

as predicting that publicly traded firms will grow faster than privately treaded firms.

More specifically, let us state three more formal and empirically testable hypotheses related to the

potential growth advantages of public firms. Given our data, we will state these hypotheses in terms of

banking firms. The first testable hypothesis is

H1: Public banks are better able than private banks to exploit new growth opportunities

provided by local economic expansion.

A positive (negative) value for the coefficient d1 in equation (1) will indicate that public banks have a

(dis)advantage at exploiting local economic growth as measured by the variable OPPORTUNITIES.

Second, the institutional characteristics of publicly traded and privately may influence their

growth rates independent of growth opportunities:

H2: Holding constant the investment opportunities provided by local economic growth, public

banks are able to grow faster than private banks.

In other words, bank growth may also be influenced by certain characteristics of “publicness” and

“privateness.” A positive (negative) value for the coefficient b in equation (1) will indicate that the public

bank characteristics provide a growth (dis)advantage, independent from local economic

OPPORTUNITIES. While “publicness” is a vague concept, we offer two examples: (a) The ability to

quickly issue new equity shares may give public banks at acquiring other banks, even in the absence of

local economic growth opportunities. (b) A higher incidence of principal-agent costs may allow public

bank managers to grow their banks more quickly, or more slowly, according to their own personal

preferences.

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Third, if publicly traded banks do exhibit growth (dis)advantages over privately held banks, this

advantage may or may not be uniform across the business cycle:

H3: Public banks are better able than private banks to exploit new growth opportunities during

all phases of the business cycle.

We can test this form of the hypothesis two different ways. First, consider the derivative

∂GROWTH/∂PUBLIC = b + d1OPPORTUNITIES from equation (1). The sign of this expression depends

not only on the values of the coefficients b and d1 associated with H1 and H2, but also on the value of the

OPPORTUNITIES variable, which reflects the state of the local business cycle. Given the attention that is

paid to credit crunches during macroeconomic downturns (e.g., Bernanke and Lown 1991, Hall 1993,

Shrieves and Dahl 1995), this test may yield valuable insight regarding public bank versus private bank

lending behavior. Second, given that internal capital is typically cheaper than external capital (Myers and

Majluf 1984), the public firm growth advantage may disappear during periods of strong economic growth

when both public and private firms are able to retain large amounts of internally generated investment

capital. We test this proposition by estimating equation (1) for above- and below-median

OPPORTUNITIES subsamples of the data and examining the sign and significance of the coefficient d1.

3.2. Principal-agent hypothesis. An asset growth or loan growth advantage for publicly traded

banks need not result in higher returns or valuations for these banks. A value-maximizing manager will

expand the size of her bank only if she expects that bank earnings will grow at least concomitantly in the

process. But a utility-maximizing manager may expand the size of the bank (e.g., by accepting value-

depleting investments) for her own satisfaction. A number of studies have linked managerial agency

motives to increased bank merger activity in the U.S. and elsewhere (see DeYoung, Evanoff and

Molyneux 2009 for a review of the post-2000 literature). The following hypothesis links faster growth at

public banks to managerial motives:

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H4: At public banks, superior growth rates do not add shareholder value, but rather are the

result of managerial utility maximization or short-term earnings focus.

We use a slightly augmented version of model (1) to test H4:

FORWARD ROAi,t+s = a + b*PUBLICi + c*OPPORTUNITIESi,t +

d2*PUBLICi*OPPORTUNITIESi,t + Controlsi,t + ui*vt + ei,t+s (2)

where FORWARD ROAi,t+s is the industry-adjusted and state-adjusted return on assets that occurs s years

after bank i’s quarter t growth opportunities. (One-year forward ROA is measured for quarters t+2

through t+5, two-year forward ROA is measured for quarters t+6 through t+9, etc.). The test coefficient

in equation (2) is d2, the sign of which must be interpreted in conjunction with the sign on d1 from

equation (1). For example, assume that we find d1 > 0 in (1), evidence that public banks grow faster than

private banks in response to exogenous growth opportunities. Then finding d2 < 0 in (2) would be

(circumstantial) evidence consistent with hypothesis H4 that the faster growth was at least partially a

manifestation of principal-agent problems associated with public ownership. In contrast, finding d2 ≥ 0 in

(2) would be (circumstantial) evidence for rejecting hypothesis H4, as it would suggest that access to

public capital markets provides real efficiencies that, at a minimum, offset any potential agency behavior.

In either case, it is important to note that the evidence generated from these tests will be only

circumstantial, for three reasons: First, we are not directly measuring the incremental public bank profits

generated by their more fully exploiting investment growth opportunities, but merely measuring the

incremental public bank profits that followed inter-temporally those investment growth opportunities.

Second, net income does not immediately or fully reflect the increased risks typically associated with

faster growth. Third, net income is an incomplete and inaccurate measure of returns to shareholders.

Because evaluating this hypothesis requires us to compare the financial performance of both public and

private firms, we cannot avail ourselves of market return data and must instead rely on accounting

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performance measures. In the analysis below, we complement these tests by breaking down forward net

income into its component parts and also by observing measures of forward risk outcomes.

4. Data

We estimate our models using quarterly data on U.S. commercial banks and bank holding

companies between 1984 and 2012. We obtain quarterly financial data for stand-alone banks from the

Reports of Condition and Income (call reports) and quarterly financial data for bank holding companies

(BHCs) and financial holding companies (FHCs) from the Federal Reserve Y-9C reports.7 (Once the data

are collected, we ignore the distinction between stand-alone banks and bank holding companies; we will

use the terms ‘bank’ or ‘banking company’ interchangeably to refer to any observation in our data.) We

begin with the entire population of banking companies that filed call reports or Y-9C reports during our

sample period. We then exclude all rural banks, recently chartered banks, and S corporation banks

because these banks face special sets of growth opportunities and/or growth restrictions.8 Banks in rural

areas (headquartered in a city or town never included in an MSA during the sample period) face growth

opportunities that are both qualitatively and quantitatively different from those faced by banks in

urbanized areas. Banks organized as subchapter S corporations face severe, self-imposed limits on equity

capital formation that will limit their rates of growth. Banks that are less than 10 years old exhibit periods

of unsustainable asset growth as they lever-up their equity capital for the first time (DeYoung and Hasan

1998).

To ensure that the test variable PUBLIC in our models is exogenous, we draw a distinction in our

data between ‘going public’ and ‘being public.’ We set PUBLIC=1 for quarterly observations of publicly

7 From the call reports dataset, we exclude all non-commercial bank financial institutions (RSSD9048 not equal to 200). From the Y9-C dataset, we exclude any holding company that does not have a commercial bank subsidiary. If a holding company contains multiple levels of affiliated banks and bank holding companies, we aggregate quarterly financial data to the top holder level. We exclude all observations with missing total assets, total loans, or equity information, and banking companies with 25% or more foreign ownership. 8 U.S. banks were prohibited from organizing as S corporations prior to 1997. S corporation status shows up initially in the call reports in the third quarter of 1997 and in the Y-9C reports in the first quarter of 1998. We classify any bank identified as an S corporation as of these dates as an S corporation for the entire 1984-1998 period.

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traded banking companies that have been listed for more than ten years. We drop all quarterly

observations of traded banks that have been listed for ten years or less.9 All remaining quarterly

observations are privately held banks for which PUBLIC=0. To identify publicly traded banking

companies, we start with the CRSP-FRB link published by the Federal Reserve Bank of New York on its

website.10 The CRSP-FRB link file covers all publicly traded banking organizations since 1990, and

provides a link between the bank identification numbers in the Y-9C and call reports (RSSD ID) and the

firm identification numbers (PERMCO) used in the Center for Research in Security Prices (CRSP)

database. The CRSP-FRB link file also contains information on the start and end dates of the RSSD ID-

PERMCO link. We supplement the New York Fed’s list using the CRSP US Stock Database from

January 1980 to December 1989, from which we extract all firms with SIC codes between 6000 and 6099

or between 6710 and 6719. We manually match these firms to banks, BHCs and FHCs in the Y-9C and

call reports by name, city and state.11

4.1. Data matching. Figure 1 shows the large degree to which size distributions of the public and

private banks overlap in our data (which excludes rural banks, recently chartered banks, and S corporation

banks). Fully half of the privately held banks are larger than the smallest publicly traded bank; similarly,

fully half of the publicly traded banks are smaller than the largest privately held bank. This substantial

size overlap allows us to construct a well-populated matched sample of private and public banks.

9 In robustness tests (not shown here, available upon request), we explore the validity of our claim that we have largely eliminated endogeneity bias by discarding observations of publicly traded banks that had been traded for less than ten years. We estimated equation (1) using three different versions of the PUBLIC dummy: PUBLIC_0 which includes all traded bank regardless of how long ago they went public; PUBLIC_10 which include only traded banks that went public more than 10 years ago; and PUBLIC_15 which includes only traded banks that went public more than 15 years ago. If banks with above average growth opportunities are more likely to go public, then we would expect both the PUBLIC coefficient and the derivative ∂%GROWTH/∂PUBLIC to be largest in the model that uses PUBLIC_0. Indeed, this is what we find. Moreover, if our 10-year threshold for defining PUBLIC is effective at removing this endogeniety bias, then we would expect little difference between the PUBLIC coefficients and the derivatives ∂%GROWTH/∂PUBLIC across the two models that use PUBLIC_10 and PUBLIC_15. Again, this is what we find. 10 See http://www.newyorkfed.org/research/banking_research/datasets.html for details. 11 We use the earliest of the following three dates to determine when a bank became a public corporation: the IPO date reported in the COMPUSTAT database, the first date with non-missing stock price in the CRSP US Stock Database, and the start date of the RSSD ID-PERMCO link in the New York Fed CRSP-FRB link file. A handful of banks delisted during our sample period and we use the latest of the following three dates to identify these events: the delisting date reported in the CRSP US Stock Database, the last date with non-missing stock price in the CRSP US Stock Database, and the end date of the RSSD ID-PERMCO link in the New York Fed CRSP-FRB link file.

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We begin by narrowing the field of banks to those inside the two dashed lines in Figure 1: banks

with assets greater than $118.9 million (the 1st percentile of the public bank size distribution) but less than

$50 billion (the crude size threshold used by U.S. bank regulators to define a systemically important

financial institution).12 This leaves us with far more private banks (156,388 bank-quarter observations of

5,048 different banks) than public banks (18,127 bank-quarter observations of 648 different banks). We

then match each of these public bank observations to a single private bank observation from the same

quarter, without replacement. The matched private bank must meet two conditions: (a) it must be no less

than half the asset size, and no more than twice the asset size, of the public bank, and (b) its exogenous

growth opportunities (measured by the Philadephia Fed’s Index of Local Economic Conditions, see

below) must be within one (quarterly cross-sectional) standard deviation of the exogenous growth

opportunities faced by the public bank. When these parameter thresholds produce more than one private

bank match, we choose the private bank that is closest to the public bank.13 We are able to find close

matches for about two-thirds of the public bank observations. The matched sample contains 24,354 bank-

quarter observations (12,177 matched pairs) of 1,937 different banks (622 public banks and 1,409 private

banks).14

As illustrated in Figures 2 through 5, our matching procedure removes large amounts of public

bank versus private bank heterogeneity from the data. Importantly, our matched samples of public and

private banking companies face (on average) nearly identical exogenous macroeconomic growth

opportunities. Although public banks remain persistently larger (on average) than their matched private

bank counterparts, these quarterly differences are statistically insignificant. The substantial reduction in

the average size of publicly traded banks (see Figure 2) reflects a wave of bank IPOs during the 1980s

12 Systemically important financial institutions (SIFIs) may enjoy a cost of capital advantage—and hence greater opportunities for growth—due to the market’s expectation that they are too big to fail (TBTF). 13 In particular, we choose the private bank observation that minimizes the following expression:

�𝐴𝑠𝑠𝑒𝑡𝑠𝑝𝑢𝑏𝑙𝑖𝑐−𝐴𝑠𝑠𝑒𝑡𝑠𝑝𝑟𝑖𝑣𝑎𝑡𝑒𝐴𝑠𝑠𝑒𝑡𝑠𝑝𝑢𝑏𝑙𝑖𝑐

�2

+ �𝑃ℎ𝑖𝑙𝑎𝑑𝑒𝑙𝑝ℎ𝑖𝑎 𝐹𝑒𝑑′𝑠 𝑖𝑛𝑑𝑒𝑥𝑝𝑢𝑏𝑙𝑖𝑐−𝑃ℎ𝑖𝑙𝑎𝑑𝑒𝑙𝑝ℎ𝑖𝑎 𝐹𝑒𝑑′𝑠 𝑖𝑛𝑑𝑒𝑥𝑝𝑟𝑖𝑣𝑎𝑡𝑒𝑃ℎ𝑖𝑙𝑎𝑑𝑒𝑙𝑝ℎ𝑖𝑎 𝐹𝑒𝑑′𝑠 𝑖𝑛𝑑𝑒𝑥𝑝𝑢𝑏𝑙𝑖𝑐

�2

.

14 Note that 622 + 1,409 = 2,031 > 1,937. This is because 94 banks in the matched sample were private banks in early years and became mature public banks in later years.

15

and 1990s (Francis, Hasan and Siregar 2009), which added relatively smaller firms to the universe of

publicly traded banking companies.

4.2. Variables. The main dependent variable in our tests is %GROWTH, the annualized

quarterly percentage growth rate of bank total assets, measured from the end of quarter t to the end of

quarter t+1.15 A substantial portion of bank growth during our sample period was driven by industry

consolidation (mergers and acquisitions); access to public capital markets may impact this ‘external’

growth differently than it affects internal or ‘organic’ growth. %GROWTH_ORGANIC is our measure of

the asset growth rate not attributable to acquisitions of other banks. We make this adjustment by

subtracting the assets of banks acquired by bank i during quarter t+1 from its actual t+1 assets before

calculating the growth rate.16 The asset growth rate attributable solely to acquisitions is then calculated as

%GROWTH_EXTERNAL = %GROWTH - %GROWTH_ORGANIC. Because loans are the chief asset at

banks, we also calculate each of these three growth rates based on total loans rather than total assets.

Our measure of exogenous growth opportunities, OPPORTUNITIES, is the percentage change

during the previous year (t-4 through t-1) of the Philadelphia Fed’s State Coincident Index for the states

in which bank i does business. For banks that operate in multiple states, we weight OPPORTUNITIES

using the share of bank i’s deposits in each state, using data from the FDIC’s Summary of Deposits

database. 17 Thus, OPPORTUNITIES exhibits both time series and cross section variation. We make the

reasonable argument that none of the individual banks in our data hold large enough state-wide market

shares to influence the state-level macroeconomic conditions captured by the Philadelphia Fed’s indices.

15 We calculate %GROWTH in nominal terms. Given that (a) this variable is based on quarterly asset and loan growth and (b) we include time fixed effects in our models, any effects of inflation bias are likely to be minimal. 16 We obtain merger and acquisition data from the website of the Federal Reserve Bank of Chicago. In a small number of cases, multiple acquirers shared the assets of a target bank, and it is unclear how many assets were allocated to bank i. In these cases, we calculate the organic growth rate of bank i as the average of its asset growth rates during the two years before the acquisition and the two years after the acquisition, excluding the acquisition event quarter. 17 Because the FDIC’s Summary of Deposits information was not available prior to 1994, OPPORTUNITIES is based solely on home-state macroeconomic data between 1984 and 1993. The mis-measurement here is likely to be minimal, however, as during this time period banks in many states were prohibited from operating across state lines anyway.

16

We employ a parsimonious econometric specification with just three additional Control variables.

The natural log of bank assets lnASSETS (in 2010 dollars) is a standard control variable in empirical

banking studies; in addition to its usual role in banking studies, lnASSETS will help soak up any

remaining size-based differences not neutralized by our matching procedure. EQUITY is the ratio of a

bank’s total (book) equity to its total assets, and is included to control for the potential growth-enhancing

effect of financial leverage. PAST ROA is the annualized (industry- and state-adjusted) return on assets

for bank i during quarters t-12 through t-5. (The adjustments are implemented by subtracting the

quarterly, home state cross sectional mean ROA from quarterly bank i ROA.) We include PAST ROA to

control for the likely growth-enhancing effects of high-quality of bank management. FORWARD ROAi,t+s

is the (industry- and state-adjusted) four-quarter return on assets that occurs s years after bank i’s quarter t

growth opportunities (one-year forward ROA is for quarters t+1 through t+4, two-year forward ROA is

for quarters t+5 through t+8, etc.).

Table 1 displays descriptive statistics for the matched sample. Our data coincide with a period of

intense industry consolidation, which was implemented largely via bank-bank mergers and acquisitions.

But because acquisitions are infrequent events, organic growth rates dominate external growth rates for

any given bank-quarter observation in our data. On average, banks grew their assets organically at a

7.55% annualized rate, compared to just a 2.37% annualized rate via acquisitions. Public banks grew

their loans more slowly than their assets (9.44% versus 9.64%) while private banks grew their loans faster

than their assets (9.98% versus 9.27%).18 Compared to private banks, public banks grew their assets

more than twice as fast via acquisition (3.24% versus 1.50%) but about 16% slower through internal

channels (6.91% versus 8.18%). Public banks earned somewhat lower (industry- and state-adjusted)

returns on their assets than private banks (mean PAST ROA of -0.04% versus +0.03%). Moreover, Figure

5 shows that public bank earnings were substantially more volatile over time than private bank earnings.

18 These differences are necessarily small: given that loans are the major category of bank assets, they will increase and decrease over time together. Private banks were able to increase their loans faster than their assets by becoming more asset-efficient over time, substituting higher yielding loans for lower yielding non-loan assets.

17

Public banks also used higher financial leverage than did private banks (mean EQUITY of 8.65% versus

8.86%), consistent with easier access to additional capital when needed.19

5. Results

We use two different techniques to estimate equation (1). When the dependent variable

is %ΔGROWTH_TOTAL or %ΔGROWTH_ORGANIC, we use ordinary least squares estimation with

quarterly data. When the dependent variable is %ΔGROWTH_EXTERNAL, we use Tobit estimation and

convert the quarterly data to an annual frequency. We do this because the data on external growth (i.e.,

growth via M&A) is highly left-censored and even for very acquisitive banks equals zero in the majority

of quarters in our data. All of the regressions include time-state fixed effects and standard errors are

clustered by time.20

5.1. Matched sample effects. We begin by examining whether and how our data matching

procedures influence the estimated model parameters and statistical inference tests. In Table 2 we

estimate model (1) using an unmatched data sample that includes all bank-quarter observations of public

and private banks with assets between $118.9 million (the 1st percentile of the distribution of public bank

assets) and $2.2 billion (the 99th percentile of the distribution private bank assets).21 Table 3 displays the

same models using the matched data sample.

Three sets of results in Tables 2 and 3 suggest that the data matching procedure provides desired

effects. First, the coefficients on lnASSETS are systematically positive in the unmatched sample

regressions, but these coefficients are variously positive, negative or non-significant in the matched

sample regressions. This is prima facie evidence that our matching procedure is substantially controlling 19 We do not perform difference-in-means tests between the public bank and private bank subsamples in Table 1. Such tests would be inappropriate given that the observations in our bank-quarter data panel are not independent observations. Moreover, given the large number of observations (N=24,354), almost any mean difference will be statistically significant and hence non-meaningful for analysis. 20 We do not use bank fixed effects because they are closely correlated with our PUBLIC test variable. Moreover, the time-state fixed effects by themselves already use up 5,750 degrees of freedom (50 states times 115 quarters) in regressions that use the full matched data sample. 21 We exclude banks between $2.2 billion and $50 billion because, without matching, these large and predominantly publicly traded banks may have an unreasonable influence on the estimated coefficients on our test variables PUBLIC and the PUBLIC*OPPORTUNITIES.

18

for asset-size effects. Second, we would expect a positive relationship between current stores of equity

capital and next-period asset growth—most especially for next-period external growth, as banks often use

equity to make acquisitions. We obtain this positive association in the matched sample regressions, but in

the unmatched sample this association is unexpectedly negative, suggesting that EQUITY is picking up

asset-size effects in the unmatched sample.22 Third, we obtain substantially better statistical fits in the

matched sample regressions. For the remainder of the paper, all of our results and findings will be

generated using the matched bank data sample.

5.2. Baseline results. The coefficients on OPPORTUNITIES are statistically positive in all of the

matched sample results in Table 3; this sensible result indicates that the average bank (public or private)

grows faster (slower) when presented with strong (weak) macroeconomic conditions. The impact is

substantial: based on the estimates in Table 3, column 1, a one-standard deviation increase in growth

opportunities is associated with a 54% increase in total asset growth.23 Hypothesis H1 posits that this

response will be stronger at public banks, and the coefficient on PUBLIC*OPPORTUNITIES provides a

test of this hypothesis. This coefficient is never statistically significant in the matched bank data; hence,

we find no evidence in Table 3 that public banks respond more strongly on average than private banks to

growth opportunities.

Hypothesis H3 posits that the links between growth and growth opportunities will vary depending

on the state of the business cycle, e.g., access to public finance may not matter during economic

expansions when both public and private banks are able to raise capital internally via retained earnings.

We test this possibility by evaluating the derivative ∂GROWTH/∂PUBLIC from the Table 3 regressions

for various values of OPPORTUNITIES from across its sample distribution. The results are displayed in

Table 4. Columns 1 and 2 indicate that public banks enjoy an asset growth advantage when

macroeconomic conditions are strong (at the 90th percentile of OPPORTUNITIES they grow their assets

22 It is well-known that large U.S. commercial banks held lower levels of equity capital than did small banks during our sample period. 23 The calculation is 1.543*3.31/9.45 = 0.540, where 1.543 is the coefficient on OPPORTUNITIES, 3.31 is the standard deviation of OPPORTUNITIES, and 9.45 is the mean value for %GROWTH_TOTAL (assets).

19

1.28 percentage points faster than private banks) but suffer from a loan growth disadvantage when

macroeconomic conditions are weak (at the 10th percentile of OPPORTUNITIES they grow their loans

1.01 percentage points slower than private banks). Given that asset growth and loan growth averaged

9.45% and 9.71%, respectively, for the banks in our data sample, these are non-trivial magnitudes. The

loan growth differential is especially important: during economic contractions, loan growth at privately

held commercial banks exceeded loan growth at publicly traded banks by a substantial margin.

The total asset and total loan growth results in columns 1 and 2 mask another important result,

because they aggregate two distinctly different growth channels. Columns 3 and 4 show that organic

growth is slower at public banks across the business cycle (e.g., public banks grow their assets 0.71

percentage points slower than private banks at the median of OPPORTUNITIES), while columns 5 and 6

show that external growth is faster at public banks across the business cycle (e.g., public banks grow their

assets 1.32 percentage points faster than private banks at the median of OPPORTUNITIES). The latter

result is consistent with hypothesis H2 but the former result is not. Hence, to the extent that access to

market funding gives public banks a growth advantage over private banks, public banks tend to

implement this advantage not via faster internal growth, but by acquiring the assets and loans of other

banks. This potentially implies the existence of an agency problem at publicly traded banks—a

preference for immediate and fast growth via acquisitions, regardless of the quality of that growth—

which we explore further below.

Two other results in Table 3 also point to important differences between the organic and external

growth channels. The coefficient on PAST ROA is positive for organic growth but zero for external

growth, while the coefficient on EQUITY is zero for organic growth but positive for external growth.

Thus, quite independent of the public versus private status of the banks in our data, retained earnings are

on average a pre-condition for organic growth while available equity capital is on average a pre-condition

for external growth. Given that public banks have easier access to capital, it is no surprise that they have

an external growth advantage over private banks.

20

5.3. Internal and external funding opportunities. We now further explore the possibility that

public banks’ access-to-capital advantage over private banks is conditional on the state of the business

cycle. Table 5 separates our matched data sample into two subsamples: quarterly observations in which

OPPORTUNITIES are above the sample median (strong macroeconomic expansion) and below the

sample median (slow macroeconomic growth and recessions). The idea is that all banks, both public and

private, have large supplies of retained earnings during periods of fast economic growth, and as such any

funding-based growth advantages held by public banks would be limited to periods of slow economic

growth during which retained earnings dry up for both public and private banks. Indeed, for the banks in

our matched data sample, internal cash flows (earnings before taxes, extraordinary items and non-cash

items) are on average about 0.66 percent of assets larger during the high growth opportunities

subsample.24 Cash flow differentials of this size have potentially powerful effects on asset growth rates.

Assuming a 40% marginal corporate tax rate, a 50% dividend payout, and 90% leverage, this 0.66 percent

of assets increase in pre-tax cash flows could support a 1.99% increase in bank growth rates

(0.66%*0.60*0.50*10). By comparison, the average annual asset growth rate for the banks in our sample

is 9.45%.

The results in Table 5 indicate that the relative responsiveness of public and private banks to

growth opportunities is conditional on the state of the local economy. In columns 1 through 4 (above-

median growth opportunities) the coefficient on PUBLIC*OPPORTUITIES is never statistically positive;

in columns 5 through 8 (below-median growth opportunities) this coefficient is always statistically

positive and economically large. During periods of slow economic growth, public banks are better able to

respond to growth opportunities than similarly situated private banks. Based on the estimates in columns

5 and 6, public bank assets grow 15.6% faster, and public bank loans grow 25.5% faster, than private

24 This mean annualized cash flow-to-assets ratio is 0.87% in the below-median OPPORTUNITES subsample and 1.54% in the above-median OPPORTUNITES subsample.

21

bank assets and loans in response to a one-standard deviation increase in growth opportunities.25 So when

local macroeconomic conditions are poor, public banks are better situated to make new loans in response

to improvements in conditions; in other words, public bank lending helps to accelerate an already

improving local economy.

5.4. Post-growth performance. Our results thus far offer support—albeit limited to certain

circumstances, such as a slow growing macro-economy—for the notion that access to public capital

markets allows firms to more effectively exploit exogenous growth opportunities. However, we have thus

far measured “effective exploitation of growth opportunities” solely in terms of banking company size

(assets, loans) but not in terms of banking company performance. The separation of ownership and

management typically required in a publicly traded firm creates the potential for principal-agent costs; in

the case of our study, managers of public banks may be more likely to pursue growth opportunities in

order to increase their own utility, even if those investments reduce the value of the bank to shareholders.

This was posited above as hypothesis H4.

We investigate this possibility by estimating equation (2) for the data subsample for which we

found moderate or strong evidence of a public bank growth advantage, i.e., weak local economic

conditions. We consider five different values for the dependent variable FORWARD ROA: one-year

ahead (ROA generated from quarter t+2 through quarter t+5), two-years ahead ROA, three-years ahead

ROA, four-years ahead ROA, and five years-ahead ROA (quarters t+18 through t+21). The null

hypothesis in these tests is d2<0, i.e., a negative coefficient on PUBLIC*OPPORTUNITIES. If the pursuit

of growth opportunities by publicly traded banks reduces profitability relative to the profitability of

private banks faced with the same growth opportunities, we cannot reject the possibility that the realized

growth advantages for public banks—revealed above in Table 5 during these same periods of slow

macroeconomic growth—were manifestations of principal-agent problems associated with public

ownership.

25 The first calculation is 0.358*0.029/0.0666 = 0.156 and the second calculation is 0.468*0.029/0.533 = 0.255, where 0.029 is the subsample standard deviations of OPPORTUNITIES, 0.0666 is the subsample mean for %GROWTH_ASSETS(total) and 0.0533 is the subsample mean for %GROWTH_LOANS(total).

22

Table 6 displays the results of these five regressions. For public banks, increased local growth

opportunities—the same growth opportunities to which public banks responded to by growing faster—are

associated with higher ROA one year forward, but lower ROA three years forward. This inter-temporal

positive-negative pattern is consistent with short-termism at publicly traded banks: managers take

advantage of current growth opportunities to make questionable loans which perform well in the short run

but grow delinquent or default in larger numbers in the future.26 However, the profit-enhancing one-year

forward effect is substantially larger than the profit-reducing three-year forward effect, which suggests

that fuller exploitation of growth opportunities increased the present value of future cash flows at public

banks—and hence, holding risk constant, increased shareholder value.

We conduct further investigation by re-estimating equation (2) after replacing FORWARD ROA

with forward values of the basic components of net income—interest income, interest expense,

noninterest income, noninterest expense, and loan loss provisions—each divided by total assets. Table 7

displays the estimated values and standard errors for the coefficient d2 from each of these regressions.

The evidence suggests that greater exploitation of grow opportunities at public banks was accompanied

by higher credit risk. While the growth-related increment to interest income increased one-year forward,

it decreased substantially three-years, four-years and five-years forward. The increments to loan loss

provisions follow a complementary pattern, decreasing at first but then increasing in the later years.

The right-hand-most column in Table 7 displays two additional calculations: the present value of

the five d2 coefficients from the FORWARD ROA regressions (0.0202) and the FORWARD

PROVISIONS/ASSETS regressions (0.0051).27 The former calculation is circumstantial evidence for

rejecting H4: during the five years following the investment opportunities that supported superior public

bank growth rates, the present value of public bank accounting profits exceeded that of the slower

26 Note that, because these test coefficients measure public bank growth relative to private bank growth during the years and quarters, these patterns cannot be attributed to regression to the mean. Nor can these patterns be attributed to cyclical effects in which ROA declines after spurts of macroeconomic growth opportunities, because our models include time fixed effects. We find quite similar results using return on equity (FORWARD ROE). 27 We used a 14% annual discount rate in these calculations. In the U.S. banking industry, annual returns on book equity are typically in the mid-teens, so the 14% discount rate is appropriate for our data. Higher and lower discount rates yielded quite similar relative results.

23

growing private banks. The latter calculation suggests that faster incremental growth at public banks was

achieved at least in part by making more loans of lower quality. Nevertheless, the ratio 0.0202/0.0051 =

3.96 indicates that the incremental earnings associated with an extra dollar of asset growth were four

times larger more than the expected asset write-offs associated with an extra dollar of asset growth. The

data suggest that, on average, public bank shareholders were made better off by faster asset and loan

growth, even in the presence of (observed) short-termism behavior and other potential (though

unobserved) principle-agent problems.

6. Robustness tests

Our main dependent variable %GROWTH_ASSETS understates asset growth at banks that pay out

relatively large portions of their earnings as dividends, relative to banks that retain relatively large portion

of their earnings. If dividend payout rates vary substantially across banks, and if this variation is

correlated with public bank ownership, then our tests may contain a bias. To investigate, we constructed

an adjusted version of %GROWTH_ASSETS in which quarterly assets are measured net of retained

earnings. As displayed in Table 8, our findings are robust to this adjustment.

Numerous state and federal deregulatory events occurred during our sample period. Our state-

time fixed effects should control on average for the effects of these shocks on asset growth and loan

growth. But given that most of these deregulatory events removed restrictions to banks’ geographic

expansion, these events also represent potentially nontrivial growth opportunities. We used two different

approaches to test whether publicly traded banks were better able than privately held banks to exploit the

temporary growth opportunities embedded in these deregulatory events. First, we replaced the

OPPORTUNITIES variable with a dummy variable that indicated whether a deregulation had occurred in

its home banking markets during the past three years. The test coefficient d1 was never statistically

different from zero in these tests. Second, we estimated our main models for pre- and post-Reigle Neal

Act (1994) subsamples. These tests indicated that public banks were statistically better able than private

banks to respond to local economic growth opportunities after the passage of Reigle-Neal but not prior to

24

this deregulation. Interstate bank acquisitions surged in the U.S. following Reigle-Neal, so this result is

consistent with our finding above that publicly traded banks tend to exploit growth opportunities via the

M&A channel. Nevertheless, we are hesitant to draw strong conclusions from either of these tests. The

dummy variable representations of state-level deregulations are crude measures at best, and these events

would have had little impact on the growth of the many banks that had previously circumvented state-

level banking restrictions by forming a multi-state bank holding company. And the results of the pre- and

post-Riegle-Neal tests could be reflecting pre- or post-1994 environmental phenomena unrelated to

regulation, such as the advent of new banking technologies and new banking strategic models. (Results

of these tests are available from the authors upon request.)

We used hand-matching techniques to draw our sample of privately held banks. Tables 9 and 10

provide clear evidence that our hand-matched results are strongly robust to using propensity score

matching (PSM) techniques. We conducted the PSM sampling separately for each quarter of the data as

follows. We began by identifying the 5th asset size percentile for the public banks and eliminated all

banks (public and private) below this size threshold. We then estimated a probit model (public = 1) for

all remaining banks, using the same set of right-hand side variables (OPPORTUNITIES, lnASSETS,

EQUITY, Past ROA) as in our main tests. The predicted probability (propensity score) from this model

was used to match one private bank with each public bank, using a 1% caliper. This approach resulted in

a substantially smaller sample, producing just 15,636 banks (7,818 matched pairs) compared to 25,354

banks from our hand-matched approach, and includes fewer than half of the publicly traded bank

observations. Our results were also largely robust to altering the parameters of the PSM approach (e.g.,

using the 10th rather than the 5th asset size percentile as a lower bound; using a 2% rather than a 1%

caliper; adding additional variables, such as the natural log of bank age and the ratio of core deposits-to-

assets, to the right-hand side of the probit model) although in a number of cases these changes resulted in

an unbalanced sample in which the means of the public bank and private bank covariates were

significantly different. (See DeFond, Erkens and Zhang (2014) for a recent discussion of the inherent

sensitivity of PSM to these types of design choices.) Given that there is no theory to guide us in

25

specifying the probit (probability of being a public bank) model and that we lose a large number of

observations using PSM, we feel more comfortable using the hand-matched approach.

7. Conclusions

Given the central role of dispersed public firm ownership in modern corporate finance, it is

surprising how little we know about the relationship between public ownership and firm growth. In this

study, we design a research methodology that avoids the two chief roadblocks that prevent scholars from

studying this issue: (a) the endogeneity of public ownership to firm growth rates and (b) the identification

of truly exogenous firm growth opportunities. We use a simple technique to run the first roadblock: we

include in our sample of publicly traded firms only those firms whose initial public offerings occurred at

least ten years in the past. This choice is supported by extant studies showing that both growth

opportunities and firm productivity peak around IPOs but retreat to normal levels by ten years after the

IPO (Chemmanur, He and Nandy 2009, Kenney, Patton and Ritter 2012). Another simple choice allows

us to run the second roadblock: the firms we study are U.S. commercial banking companies. Because

these firms are constrained by regulation to pursue investment opportunities within a narrow range of

financial services, and because these opportunities (e.g., loans, payments services) are linked closely to

economic activity in the states or regions in which they operate, local macroeconomic growth rates

provide a natural measure of growth opportunities that are exogenous to any given bank. This especially

clean setup should make our empirical findings generalizable to non-financial firms.

The U.S. banking industry provides a good laboratory for testing the impact of public ownership

on firm growth for several other important reasons. The industry experienced numerous exogenous

shocks to growth opportunities during our 1984-2012 sample as first states and then the federal

government relaxed regulatory restrictions on banks’ geographic footprints. These deregulatory actions

provide unusual scope for geographic growth during our sample period due to the release of banks’ pent-

up demand for expansion. In addition, while in most industries large firm size and public ownership

typically go hand-in-hand, the size distributions of public traded banks and privately held banks overlap

26

substantially—as do the product lines, customer bases, production processes, regulatory conditions and

other characteristics of public and private banks. Thus, the PUBLIC dummy variable that we use for

testing for differences in public and private bank growth rates carries with it much less extraneous and

potentially biasing information than in most if not all nonfinancial industries. Finally, due to the

consolidation of the U.S. banking industry during our sample period, our data contain a rich mix of

organic growth and external (M&A) growth; this facilitates large sample tests of public ownership’s

impact on multiple channels of firm growth.

We find a number of robust patterns in the data. Publicly banks were better able than private

banks to exploit changes in local growth opportunities, both only during periods of slow macroeconomic

growth when private banks internal sources of capital (retained earnings) dry up. Outside of these

periods, the data suggest that public and private banks were equally able to exploit local growth

opportunities—presumably funded by internal capital. Hence, publicly traded status appears to have been

a necessary but not sufficient condition for public banks to exercise the latent advantages of access to

public equity capital markets. Our analysis of the data rejects the notion that faster growth at publicly

traded firms is systematically driven by principal-agent costs (e.g., empire building), although we do find

some evidence suggesting that faster growth is associated with a short-term investment focus. The strong

growth opportunities to which public banks respond are followed by periods of reduced interest income

and increased loan losses at these banks, but public bank earnings during these periods more than offset

the necessary asset write-downs.

We also find a distinct growth channel dichotomy, with public banks more likely to grow their

assets and loans through external channels (M&As) and private banks more likely to grow their assets and

loans organically. This is consistent with the fact that newly issued equity shares are the standard

currency in bank acquisitions. It is also consistent with the surge of commercial bank IPOs in the U.S.

during the late-1980s and throughout the 1990s—banks wishing to take advantage of the M&A growth

opportunities being made possible by deregulation gained access to the currency (cheaper equity capital)

necessary to make those transactions by going public.

27

We are also able to show that the well-known pro-cyclicality of bank loan growth is asymmetric

for public and private banks. Public banks grow their loans substantially slower than do private banks

during economic recessions (i.e., at and below the 10th percentile of local macroeconomic growth).

Hence, public bank loan supply is more pro-cyclical than private bank loan supply—a potentially

important finding as bank regulators (i.e., Basel III) seek to impose counter-cyclicality on bank equity

capital requirements. This result infers that privately held banks may be disproportionately important for

maintaining credit supply during economic downturns.

28

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30

Table 1 Descriptive statistics for variables used to estimate equations (1) and (2). Matched sample of 24,354 quarterly observations of 1,937 different U.S. commercial banking companies between 1984:Q1 and 2012:Q3. %GROWTH_TOTAL, %GROWTH_ORGANIC and %GROWTH_EXTERNAL are annualized quarterly growth rates (for either bank assets or bank loans, as indicated), where ORGANIC refers to assets or loans produced internally by the bank and EXTERNAL refers to assets or loans acquired via M&A. OPPORTUNITES is the percentage change in the Philadelphia Fed’s State Coincident Index from quarter t-4 through quarter t-1, weighted by the share of a bank’s deposits that it draws from each state. ASSETS and EQUITY are quarterly balance sheet values measured in thousands of 2010 dollars. Past ROA is the average annualized return on assets for quarters t-12 to t-5. Past ROA and Forward ROA are annualized averages over the indicated quarterly time spans. All variables, with the exception of dummy variables and logged variables, are winsorized at the 1st and 99th percentiles of their quarterly sample distributions.

All Banks Public Banks Private Banks N = 24,354 N = 12,177 N = 12,177 mean sd mean sd mean sd

%GROWTH_TOTAL (assets) 9.45% 24.93% 9.64% 25.94% 9.27% 23.87% %GROWTH_TOTAL (loans) 9.71% 26.19% 9.44% 26.58% 9.98% 25.79% %GROWTH_ORGANIC (assets) 7.55% 19.90% 6.91% 19.46% 8.18% 20.31% %GROWTH_ORGANIC (loans) 7.78% 21.31% 6.68% 20.18% 8.88% 22.33% %GROWTH_EXTERNAL (assets) 2.37% 14.10% 3.24% 16.84% 1.50% 10.62% %GROWTH_EXTERNAL (loans) 2.38% 14.44% 3.26% 17.29% 1.50% 10.80% OPPORTUNITIES 1.92% 3.31% 1.89% 3.35% 1.95% 3.27% ASSETS ($1,000) 2,860,000 4,490,000 3,390,000 5,030,000 2,330,000 3,810,000 lnASSETS 14.31 0.99 14.46 1.05 14.17 0.90 EQUITY ($1,000) 8.76% 2.88% 8.65% 2.44% 8.86% 3.27% PAST ROA (t-12 to t-5) -0.01% 0.68% -0.04% 0.68% 0.03% 0.67% FORWARD ROA (t+2 to t+5) -0.04% 1.01% -0.06% 1.03% -0.02% 0.99% FORWARD ROA (t+6 to t+9) -0.05% 1.01% -0.06% 1.02% -0.03% 1.00% FORWARD ROA (t+10 to t+13) -0.04% 0.99% -0.07% 1.02% -0.02% 0.95% FORWARD ROA (t+14 to t+17) -0.05% 0.99% -0.07% 1.01% -0.03% 0.97% FORWARD ROA (t+18 to t+21) -0.05% 0.99% -0.08% 1.02% -0.02% 0.97%

31

Table 2 Estimation results for equation (1) using unmatched data for U.S. commercial banking companies between 1984 and 2012. Columns 1, 2, 4 and 5 display the results of OLS estimations for 159,852 quarterly observations of 5,181 different banks. Columns 3 and 6 display the results of Tobit estimations for 36,981 annual observations of 4,400 different banks. All regressions contain state-time fixed effects. Standard errors are clustered by time and appear in parentheses. ***, ** and * indicate statistically different from zero at the 1%, 5% and 10% levels, respectively.

asset growth loan growth

[1] [2] [3] [4] [5] [6]

Dependent variable: total growth

organic growth

external growth

total growth

organic growth

external growth

Estimation technique: OLS OLS Tobit OLS OLS Tobit Data frequency: quarterly quarterly annual quarterly quarterly annual PUBLIC 0.00123 -0.00744*** 0.0349 -0.00572** -0.0141*** 0.0470*

(0.00338) (0.00261) (0.0223) (0.00275) (0.00228) (0.0249)

OPPORTUNITIES 0.941*** 0.924*** 0.371** 1.374*** 1.358*** 0.662***

(0.106) (0.105) (0.149) (0.115) (0.124) (0.169)

PUBLIC*OPPORTUNITIES 0.310*** 0.181** 1.834*** 0.345*** 0.201*** 1.820***

(0.0947) (.0715) (0.594) (0.0998) (0.075) (0.663)

lnASSETS 0.00653*** 0.00282** 0.1000*** 0.00717*** 0.00351** 0.114***

(0.00152) (0.00132) (0.00656) (0.00151) (0.00136) (0.0074)

EQUITY -0.0219 -0.0691) -0.502*** -0.182*** -0.245*** -0.405**

(0.0612) (0.0579) (-0.159) (0.0606) (0.0561) (0.179)

PAST ROA 2.250*** 2.061*** 0.253 2.329*** 2.197*** 0.697

(0.139) (0.131) (0.725) (0.185) (0.173) (0.821)

Constant -0.0163 0.026 -1.879*** -0.0128 0.0314* -2.152***

(0.0216) (0.0186) (-0.0901) (0.0207) (0.0186) (0.102)

Observations 159,852 159,852 36,981 159,852 159,852 36,981 Banks 5,181 5,181 4,400 5,181 5,181 4,400 R-squared 0.090 0.108 0.0296 0.104 0.122 0.0309

32

Table 3 Estimation results for equation (1) using a matched data sample for U.S. commercial banking companies between 1984 and 2012. Columns 1, 2, 4 and 5 display the results of OLS estimations for 24,354 quarterly observations of 1,937 different banks. Columns 3 and 6 display the results of Tobit estimations for 5,493 annual observations of 1,267 different banks. All regressions contain state-time fixed effects. Standard errors are clustered by time and appear in parentheses. ***, ** and * indicate statistically different from zero at the 1%, 5% and 10% levels, respectively.

asset growth loan growth

[1] [2] [3] [4] [5] [6]

total

growth organic growth

external growth

total growth

organic growth

external growth

Estimation technique: OLS OLS Tobit OLS OLS Tobit Data frequency: quarterly quarterly annual quarterly quarterly annual

PUBLIC 0.00436 -0.00808** 0.0738*** -0.00657* -0.0186*** 0.0774***

(0.00383) (-0.00317) (0.0135) (0.00338) (0.00269) (0.0147)

OPPORTUNITIES 1.543*** 1.446*** 1.034*** 1.836*** 1.734*** 1.183***

(0.312) (0.224) (0.279) (0.285) (0.231) (0.303)

PUBLIC*OPPORTUNITIES 0.163 0.0384 0.0981 0.142 0.0194 0.0746

(0.109) (0.0819) (0.361) (0.123) (0.0798) (0.392)

lnASSETS 0.00202 -0.00372** 0.0739*** 0.00575** -0.00203 0.0825***

(0.0023) (0.00186) (0.00579) (0.0025) (0.00208) (0.00629)

EQUITY 0.281** 0.164 0.329* 0.204 0.0574 0.540**

(0.122) (0.110) (0.199) (0.124) (0.113) (0.214)

PAST ROA 2.564*** 2.253*** 0.267 2.216*** 1.991*** 0.38

(0.312) (0.310) (0.932) (0.369) (0.354) (1.017)

Constant 0.00782 0.0905*** -1.421*** -0.0362 0.0778** -1.592***

(0.0363) (0.0308) (0.0911) (0.0385) (0.0334) (0.0993)

Observations 24,354 24,354 5,493 24,354 24,354 5,493 Banks 1,937 1,937 1,267 1,937 1,937 1,267 R-squared 0.238 0.248 0.101 0.250 0.271 0.101

33

Table 4 Estimated values for the derivative ∂GROWTH/∂PUBLIC derived from the Table 3 regressions and evaluated across the sample distribution of OPPORTUNITIES. p-values appear in parentheses. ***, ** and * indicate statistically different from zero at the 1%, 5% and 10% levels, respectively.

total growth organic growth external growth [1] [2] [3] [4] [5] [6] Derivative evaluated for OPPORTUNITIES at percentile:

Assets loans assets loans assets loans

10th percentile 0.04% -1.01%** -0.90%** -1.91%*** 1.23%*** 1.30%***

(0.94) (0.04) (0.04) (0.00) (0.00) (0.00)

25th percentile 0.51% -0.59%* -0.79%*** -1.85%*** 1.28%*** 1.34%*** (0.17) (0.08) (0.01) (0.00) (0.00) (0.00) 50th percentile 0.84%** -0.30% -0.71%** -1.81%*** 1.32%*** 1.37%*** (0.03) (0.48) (0.02) (0.00) (0.00) (0.00) 75th percentile 1.09%** -0.09% -0.65%* -1.78%*** 1.34%*** 1.39%*** (0.03) (0.88) (0.07) (0.00) (0.00) (0.00) 90th percentile 1.28%** 0.08% -0.61% -1.76%*** 1.36%*** 1.40%*** (0.03) (0.91) (0.15) (0.00) (0.00) (0.00)

34

Table 5 Estimation results for equation (1) using two subsamples drawn from a matched sample of U.S. commercial banking companies. Columns 1 through 4 display the results of OLS estimations for 12,178 quarterly observations of above-median growth opportunities. Columns 5 through 8 display the results of OLS estimations for 12,176 quarterly observations of below-median growth opportunities. All regressions contain state-time fixed effects. Standard errors are clustered by time and appear in parentheses. ***, ** and * indicate statistically different from zero at the 1%, 5% and 10% levels, respectively.

Above median OPPORTUNITIES Below median OPPORTUNITIES

[1] [2] [3] [4] [5] [6] [7] [8]

total assets organic

assets total loans organic loans total assets organic

assets total loans organic loans

PUBLIC 0.0283 0.0244** -0.000313 -0.00472 0.00887* -0.00559 0.00325 -0.0110***

(0.0172) (0.0113) (0.0238) (0.0142) (0.00519) (0.00433) (0.00497) (0.00410)

OPPORTUNITIES 2.465*** 2.259*** 1.898*** 1.700*** 0.822*** 0.825*** 1.147*** 1.176***

(0.509) (0.328) (0.589) (0.422) (0.273) (0.212) (0.263) (0.221)

PUBLIC*OPPS. -0.430 -0.724*** -0.144 -0.413 0.358*** 0.197* 0.468*** 0.301***

(0.397) (0.236) (0.606) (0.345) (0.127) (0.108) (0.112) (0.0949)

lnASSETS 0.00722** -0.000231 0.0109*** 0.00154 -0.00367 -0.0077*** -0.00075 -0.0068***

(0.00360) (0.00295) (0.00409) (0.00325) (0.00255) (0.00215) (0.00268) (0.00236)

EQUITY 0.140 0.0496 -0.0797 -0.155 0.418*** 0.273** 0.441*** 0.238*

(0.186) (0.162) (0.182) (0.159) (0.150) (0.136) (0.152) (0.141)

PAST ROA 2.543*** 2.288*** 2.038*** 1.842*** 2.621*** 2.243*** 2.422*** 2.167***

(0.554) (0.576) (0.643) (0.622) (0.301) (0.304) (0.366) (0.356)

Constant -0.104* 0.00498 -0.0867 0.0465 0.0821* 0.144*** 0.0296 0.126***

(0.0605) (0.0489) (0.0653) (0.0497) (0.0422) (0.0353) (0.0411) (0.0368)

Observations 12,178 12,178 12,178 12,178 12,176 12,176 12,176 12,176 Banks 1,532 1,532 1,532 1,532 1,365 1,365 1,365 1,365 R-squared 0.282 0.290 0.265 0.278 0.198 0.207 0.227 0.246

35

Table 6 Estimation results for equation (2). Data for below-median growth opportunity subsample from a matched sample of U.S. commercial banking companies between 1984 and 2012. All regressions contain state-time fixed effects. Standard errors are clustered by time and appear in parentheses. ***, ** and * indicate statistically different from zero at the 1%, 5% and 10% levels, respectively. [1] [2] [3] [4] [5]

ROA one year forward (t+2 to

t+5)

ROA two years forward (t+6 to

t+9)

ROA three years forward (t+10

to t+13)

ROA four years forward (t+14

to t+17)

ROA five years forward (t+18

to t+21) PUBLIC -0.000693*** -0.000664** -0.000576*** -0.000252 -0.000446** (0.000264) (0.000292) (0.000190) (0.000241) (0.000209) OPPORTUNITIES 0.0133 -0.00801 -0.0222 -0.0512*** -0.119*** (0.0271) (0.0190) (0.0174) (0.0139) (0.0300) PUBLIC*OPPS. 0.0484*** -0.00417 -0.0172*** -0.0134 0.000865 (0.0137) (0.00787) (0.00572) (0.00954) (0.0177) lnASSETS 3.21e-05 0.000220 0.000549*** 0.000562*** 0.000322 (0.000301) (0.000270) (0.000128) (0.000121) (0.000268) EQUITY 0.0792*** 0.0724*** 0.0580*** 0.0557*** 0.0464*** (0.0105) (0.00525) (0.00400) (0.00449) (0.00672) PAST ROA 0.365*** 0.284*** 0.282*** 0.204*** 0.194*** (0.0252) (0.0387) (0.0286) (0.0276) (0.0285) Constant -0.00793* -0.0101** -0.0135*** -0.0136*** -0.00875** (0.00437) (0.00396) (0.00188) (0.00184) (0.00425) Observations 11,874 10,547 9,048 7,247 5,717 Banks 1,333 1,224 1,089 931 798 R-square 0.208 0.203 0.228 0.238 0.225

36

Table 7

Selected estimation results for equation (2). Dependent variable indicated in the left-hand column. Cells contain the estimated coefficients on PUBLIC*OPPORTUNITIES. Data for below-median growth opportunity subsample from a matched sample of U.S. commercial banking companies between 1984 and 2012. All regressions contain state-time fixed effects. Standard errors are clustered by time and appear in parentheses. ***, ** and * indicate statistically different from zero at the 1%, 5% and 10% levels, respectively.

Time period on which dependent variable is observed:

[1] [2] [3] [4] [5]

one year forward

(t+2 to t+5)

two years forward

(t+6 to t+9)

three years forward (t+10 to

t+13)

four years forward (t+14 to

t+17)

five years forward (t+18 to

t+21)

Dependent variable

Coefficients on PUBLIC*OPPORTUNITIES

Present value of row

(discount rate = 14%)

Return on assets 0.0484*** -0.00417 -0.0172*** -0.0134 0.000865

0.0202

(0.0137) (0.00787) (0.00572) (0.00954) (0.0177)

Interest income/assets

0.00781* -0.00549 -0.0180*** -0.0225*** -0.0391***

(0.00454) (0.00527) (0.00361) (0.00586) (0.0133)

Noninterest income/assets

0.00957** 0.00655 0.00797 0.00530 0.0339**

(0.00456) (0.00580) (0.00667) (0.00757) (0.0143)

Interest expense/assets

0.00301 0.00291 -0.00142 -0.00318 -0.00390

(0.00355) (0.00304) (0.00255) (0.00379) (0.0145)

Noninterest expense/assets

-0.00608 0.00278 0.00479 0.00220 0.0257

(0.00864) (0.00579) (0.00814) (0.00807) (0.0164)

Provisions expense/assets

-0.0212** 0.0147*** 0.00820* 0.0103* 0.00141

0.0051

(0.00926) (0.00416) (0.00459) (0.00600) (0.00965)

37

Table 8 Estimation results for equation (1) using both total asset growth (columns 1, 3, 5) and asset growth net of retained earnings (columns 2, 4, 5). All regressions contain state-time fixed effects. Standard errors are clustered by time and appear in parentheses. ***, ** and * indicate statistically different from zero at the 1%, 5% and 10% levels, respectively.

full sample above median growth

opportunities below median growth

opportunities

[1] [2] [3] [4] [5] [6]

asset growth

asset growth adjusted for

retained earnings asset growth

asset growth adjusted for

retained earnings asset growth

asset growth adjusted for

retained earnings

PUBLIC 0.00436 0.00591 0.0283 0.0270 0.00887* 0.00964*

(0.00383) (0.00373) (0.0172) (0.0171) (0.00519) (0.00503)

OPPORTUNITES 1.543*** 1.403*** 2.465*** 2.381*** 0.822*** 0.607**

(0.312) (0.311) (0.509) (0.503) (0.273) (0.274)

PUBLIC*OPP. 0.163 0.104 -0.430 -0.411 0.358*** 0.253**

(0.109) (0.104) (0.397) (0.393) (0.127) (0.126)

lnASSETS 0.00202 0.00242 0.00722** 0.00747** -0.00367 -0.00302

(0.00230) (0.00227) (0.00360) (0.00359) (0.00255) (0.00251)

EQUITY 0.281** 0.256** 0.140 0.134 0.418*** 0.378***

(0.122) (0.117) (0.186) (0.183) (0.150) (0.144)

Past ROA 2.564*** 2.300*** 2.543*** 2.337*** 2.621*** 2.299***

(0.312) (0.307) (0.554) (0.542) (0.301) (0.298)

Constant 0.00782 0.00230 -0.104* -0.111* 0.0821* 0.0732*

(0.0363) (0.0357) (0.0605) (0.0600) (0.0422) (0.0415)

Observations 24,354 24,351 12,178 12,178 12,176 12,173 Banks 1,937 1, 937 1, 532 1.532 1,365 1,365 R-squared 0.238 0.233 0.282 0.281 0.198 0.191

38

Table 9 Estimation results for equation (1) using two subsamples drawn from a propensity matched sample of U.S. commercial banking companies. Columns 1 through 4 display the results of OLS estimations for 7,828 quarterly observations of above-median growth opportunities. Columns 5 through 8 display the results of OLS estimations for 7,808 quarterly observations of below-median growth opportunities. All regressions contain state-time fixed effects. Standard errors are clustered by time and appear in parentheses. ***, ** and * indicate statistically different from zero at the 1%, 5% and 10% levels, respectively.

Above median OPPORTUNITIES Below median OPPORTUNITIES

[1] [2] [3] [4] [5] [6] [7] [8]

total assets

organic assets total loans

organic loans total assets

organic assets total loans

organic loans

PUBLIC 0.0236 0.0206 0.00139 6.04e-05 0.00957 -0.00481 0.00451 -0.00944*

(0.0283) (0.0180) (0.0315) (0.0155) (0.00691) (0.00517) (0.00702) (0.00552)

OPPORTUNITES 1.769*** 1.821*** 1.577** 1.648*** 0.932*** 1.101*** 1.260*** 1.400***

(0.608) (0.502) (0.675) (0.583) (0.334) (0.273) (0.260) (0.275)

PUBLIC*OPPS. -0.485 -0.723* -0.183 -0.406 0.434*** 0.245* 0.513*** 0.302*

(0.691) (0.406) (0.814) (0.365) (0.154) (0.147) (0.163) (0.155)

lnASSETS 0.00866* 0.00314 0.0139*** 0.00711* -0.00308 -0.00749*** 0.00151 -0.00413

(0.00475) (0.00395) (0.00472) (0.00388) (0.00345) (0.00260) (0.00360) (0.00269)

EQUITY 0.107 0.0324 -0.0834 -0.125 0.481*** 0.277* 0.592*** 0.322**

(0.209) (0.175) (0.230) (0.199) (0.177) (0.158) (0.171) (0.162)

PAST ROA 2.228*** 1.864*** 1.972*** 1.613** 2.015*** 1.791*** 1.681*** 1.574***

(0.680) (0.603) (0.715) (0.619) (0.508) (0.473) (0.552) (0.508)

Constant -0.0888 -0.0214 -0.115 -0.0341 0.0703 0.145*** -0.0135 0.0836**

(0.0721) (0.0607) (0.0727) (0.0613) (0.0529) (0.0405) (0.0491) (0.0366)

Observations 7,828 7,828 7,828 7,828 7,808 7,808 7,808 7,808 Banks 1,324 1,324 1,324 1,324 1,168 1,168 1,168 1,168 R-squared 0.354 0.363 0.342 0.366 0.250 0.271 0.298 0.324

39

Table 10 Estimation results for equation (2). Data for below-median growth opportunity subsample from a propensity matched sample of U.S. commercial banking companies between 1984 and 2012. All regressions contain state-time fixed effects. Standard errors are clustered by time and appear in parentheses. ***, ** and * indicate statistically different from zero at the 1%, 5% and 10% levels, respectively. [1] [2] [3] [4] [5]

ROA one year forward (t+2 to

t+5)

ROA two years forward (t+6 to

t+9)

ROA three years forward (t+10

to t+13)

ROA four years forward (t+14

to t+17)

ROA five years forward (t+18

to t+21) PUBLIC -0.000823*** -0.000544** -0.000426** -0.000573 -0.000671** (0.000287) (0.000232) (0.000190) (0.000388) (0.000326) OPPORTUNITIES 0.00230 -0.0204 -0.0434* -0.0731** -0.155*** (0.0274) (0.0254) (0.0237) (0.0299) (0.0371) PUBLIC*OPPS. 0.0562*** 0.0222*** 0.0157** 0.0135 -0.00613 (0.0181) (0.00823) (0.00718) (0.0119) (0.0236) lnASSETS 1.25e-05 0.000415 0.000479** 0.000510*** 0.000178 (0.000306) (0.000262) (0.000215) (0.000189) (0.000333) EQUITY 0.0685*** 0.0682*** 0.0623*** 0.0497*** 0.0296** (0.0143) (0.00864) (0.00810) (0.00780) (0.0122) PAST ROA 0.355*** 0.252*** 0.249*** 0.195*** 0.183*** (0.0314) (0.0460) (0.0380) (0.0432) (0.0620) Constant -0.00680 -0.0129*** -0.0132*** -0.0126*** -0.00556 (0.00428) (0.00355) (0.00290) (0.00290) (0.00509)

Observations 7,597 6,733 5,753 4,573 3,559 Banks 1,146 1,041 922 797 685 R-square 0.239 0.233 0.251 0.258 0.258

40

Figure 1

41

Figure 2

Figure 3

$0

$2,000,000

$4,000,000

$6,000,000

$8,000,000

$10,000,000

$12,000,000

$14,000,00019

8403

1986

03

1988

03

1990

03

1992

03

1994

03

1996

03

1998

03

2000

03

2002

03

2004

03

2006

03

2008

03

2010

03

2012

03

Mean quarterly assets, prior to matchingData in 2010 $1,000.

Public BanksPrivate Banks

$0

$2,000,000

$4,000,000

$6,000,000

$8,000,000

$10,000,000

$12,000,000

$14,000,000

1984

03

1986

03

1988

03

1990

03

1992

03

1994

03

1996

03

1998

03

2000

03

2002

03

2004

03

2006

03

2008

03

2010

03

2012

03Mean quarterly assets, matched sample

Data in 2010 $1,000. Dashed lines indicate 0.5 standard deviations around public bank quarterly means.

Public BanksPrivate Banks

42

Figure 4

Figure 5

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

1984

03

1986

03

1988

03

1990

03

1992

03

1994

03

1996

03

1998

03

2000

03

2002

03

2004

03

2006

03

2008

03

2010

03

2012

03

Percent change in mean quarterly Coincident Index, prior to matching

Public Banks

Private Banks

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

1984

03

1986

03

1988

03

1990

03

1992

03

1994

03

1996

03

1998

03

2000

03

2002

03

2004

03

2006

03

2008

03

2010

03

2012

03Percent change in mean quarterly Coincident

Index, matched sample

Public Banks

Private Banks

43

Figure 6

-0.40%

-0.30%

-0.20%

-0.10%

0.00%

0.10%

0.20%

0.30%

0.40%

1984

03

1986

03

1988

03

1990

03

1992

03

1994

03

1996

03

1998

03

2000

03

2002

03

2004

03

2006

03

2008

03

2010

03

2012

03

Mean quarterly industry-State adjusted ROA, matched sample

Private banks

Public banks